Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

IEICE Electronics Express

Self-adjustable offset min-sum algorithm for ISDB-S2 LDPC decoder

Wen Ji, Makoto Hamaminato, Hiroshi Nakayama, Satoshi Goto

Published Jan 1, 2010• 5 citations
Publisher

Abstract

In this paper, a novel self-adjustable offset min-sum LDPC decoding algorithm is proposed for ISDB-S2 application. We present for the first time a uniform approximation of the check node operation through mathematical induction on Jacobian logarithm and theoretically shows that the offset value is mainly dependent on the difference of the two most unreliable inputs from the bit nodes, which makes the offset value adjustable during the iterative decoding procedure. Simulation results for all 11 code rates of ISDB-S2 demonstrate that the proposed method can achieve an average of 0.15dB gain compared to the Min-sum based algorithms, and consumes only 1.21% computation complexity compared to BP-based algorithms in the best case.

DOI
10.1587/elex.7.1283

Related papers

A novel hardware-friendly self-adjustable offset min-sum algorithm for ISDB-S2 LDPC decoder

Wen Ji, Makoto Hamaminato, Hiroshi Nakayama +1 more

In this paper, a novel self-adjustable offset min-sum LDPC decoding algorithm is proposed for ISDB-S2 (Integrated Services Digital Broadcasting via Satellite — Second Generation) application. We present for the first time a uniform approximation of the check node operation through mathematical induction on Jacobian logarithm. The approximation theoretically shows that the offset value is mainly dependent on the difference between the two most unreliable inputs from the bit nodes and the algorithm proposed can adjust the offset value according to the inputs during the iterative decoding procedure. Simulation results for all 11 code rates of ISDB-S2 demonstrate that the proposed method can achieve an average of 0.15dB gain under the same Bit Error Rate (BER) performance, compared to the Min-sum based algorithms, and consumes only 1.21% computation complexity compared to BP-based algorithms in the best case.

European Signal Processing Conference (EUSIPCO)•Aug 1, 2010•2 citations

A modified Offset Min-Sum decoding algorithm for LDPC codes

Meng Xu, Jianhui Wu, Meng Zhang

In this paper a modified Offset Min-Sum decoding algorithm for Low-Density Parity Check Codes is presented. In this modified algorithm, the offset factor in the original Offset Min-Sum algorithm is adjusted iteratively with the assistance of the check-node computation outputs of the Normalized Min-Sum algorithm. As a result, the offset factor in our modified algorithm can be calculated adaptively and more efficiently according to the previous decoding procedure, which can minimize the decoding degradation compared with the Belief Propagation decoding algorithm. The simulation results show that, compared with the original Offset Min-Sum decoding algorithm, our modified algorithm can achieve noticeable performance improvement with minor extra hardware complexity. For example, when BER is 10−5, our algorithm can achieve 0.1dB and 0.2dB decoding gain over Offset Min-Sum algorithm for regular and irregular LDPC codes respectively.

•Jul 1, 2010•29 citations

Modified min-sum decoding algorithm for LDPC codes based on classified correction

Zhong Zhou, Yunzhou Li, Xiang Chen +2 more

In this paper, a modified min-sum decoding algorithm based on classified correction is proposed for low density parity check (LDPC) codes. Different from the single correction in the normalized belief propagation (BP)-based and offset BP-based algorithms [4], the proposed algorithm utilizes two corrections for both minimum and sub-minimum magnitudes of input messages in check nodes. These two correction factors can be obtained by analyzing the offset of updated messages in check nodes between the BP and the min-sum algorithms associated with check node degree. Simulation results show that the proposed algorithm can achieve performance very close to that of the BP algorithm. Furthermore, the FPGA implementation of this algorithm can reach a throughput of 200 Mbps at BER=10-6with lower complexity and fewer resources than the BP algorithm.

International Conference on Communications and Networking in China (ChinaCom)•Aug 1, 2008•9 citations

Genetic Algorithm-Optimized Adaptive Offset Min-Sum LDPC Decoder for 5G- NR Base Graph 2 Codes

Anwer Sabah Ahmed, Yousif Jawad Kadhim Nukhailawi, Heyam A. Marzog +1 more

Abstract This paper proposes a resource-efficient Adaptive Offset Min-Sum (A-OMS) LDPC decoder for 5G-NR Base Graph 2 (BG2) codes. A Genetic Algorithm (GA) is employed to optimize the offset factor within the check-node update, addressing the difficulty of selecting a globally optimal offset for the irregular BG2 structure. The GA operates as a meta-heuristic optimizer that minimizes the bit-error rate (BER) over a wide range of signal-to-noise ratios (SNRs). Simulation results show that the proposed GA-optimized A-OMS decoder achieves performance within 0.2–0.4 dB of belief-propagation decoding, while providing coding gains of 0.3–0.6 dB and up to a 4.6× BER reduction compared with conventional fixed-offset OMS. These improvements are obtained with approximately 35% of the computational complexity of BP decoding. The proposed approach offers a practical balance between performance and implementation efficiency for next-generation 5G-NR receivers.

•Jan 28, 2026•1 citation

Adaptive offset min-sum algorithm for low-density parity check codes

Ming Jiang, Chunming Zhao, Li Guo Zhang +1 more

An improvement is presented to the offset min-sum decoding algorithm for low-density parity check codes. The proposed algorithm introduces a more efficient adjustment for check-node update computation in view of different minimum values. According to the optimal correction factor of normalized min-sum algorithm, we can determine the adaptive offset item. The improved algorithm achieves a noticeable performance gain with only a modest increase in computation complexity.

IEEE Communications Letters•Jun 1, 2006•43 citations

Adaptive normalized min-sum algorithm for LDPC decoding

Liwen Fan, Changyong Pan, Kewu Peng +1 more

This paper proposes an adaptive normalized min-sum algorithm for the decoding of low-density parity check (LDPC) codes, which utilizes an adaptive normalization factor to improve the accuracy of the soft information transferred during the iterative decoding process, and provides superior performance accordingly. This adaptive normalization factor can be adjusted dynamically and adaptively at each decoding iteration according to a look-up table obtained via training and simulation. Its implementation facility and independence from the channel characteristics make the proposed adaptive normalized min-sum algorithm expect a wide application. Simulation results show that the proposed algorithm can achieve performance much closer to the sum-product algorithm and a coding gain of around 0.2dB compared to the conventional normalized min-sum algorithm for DVB-S2's rate 2/5 and 3/5 LDPC codes over the additive white Gaussian noise (AWGN) channel.

International Wireless Communications and Mobile Computing Conference (IWCMC)•Jul 1, 2013•23 citations
In this paper, a novel self-adjustable offset min-sum LDPC decoding algorithm is proposed for ISDB-S2 application. We present for the first time a uniform approximation of the check node operation through mathematical induction on Jacobian logarithm and theoretically shows that the offset value is mainly dependent on the difference of the two most unreliable inputs from the bit nodes, which makes the offset value adjustable during the iterative decoding procedure. Simulation results for all 11 code rates of ISDB-S2 demonstrate that the proposed method can achieve an average of 0.15dB gain compared to the Min-sum based algorithms, and consumes only 1.21% computation complexity compared to BP-based algorithms in the best case.
In this paper, a novel self-adjustable offset min-sum LDPC decoding algorithm is proposed for ISDB-S2 (Integrated Services Digital Broadcasting via Satellite — Second Generation) application. We present for the first time a uniform approximation of the check node operation through mathematical induction on Jacobian logarithm. The approximation theoretically shows that the offset value is mainly dependent on the difference between the two most unreliable inputs from the bit nodes and the algorithm proposed can adjust the offset value according to the inputs during the iterative decoding procedure. Simulation results for all 11 code rates of ISDB-S2 demonstrate that the proposed method can achieve an average of 0.15dB gain under the same Bit Error Rate (BER) performance, compared to the Min-sum based algorithms, and consumes only 1.21% computation complexity compared to BP-based algorithms in the best case.
In this paper a modified Offset Min-Sum decoding algorithm for Low-Density Parity Check Codes is presented. In this modified algorithm, the offset factor in the original Offset Min-Sum algorithm is adjusted iteratively with the assistance of the check-node computation outputs of the Normalized Min-Sum algorithm. As a result, the offset factor in our modified algorithm can be calculated adaptively and more efficiently according to the previous decoding procedure, which can minimize the decoding degradation compared with the Belief Propagation decoding algorithm. The simulation results show that, compared with the original Offset Min-Sum decoding algorithm, our modified algorithm can achieve noticeable performance improvement with minor extra hardware complexity. For example, when BER is 10−5, our algorithm can achieve 0.1dB and 0.2dB decoding gain over Offset Min-Sum algorithm for regular and irregular LDPC codes respectively.
In this paper, a modified min-sum decoding algorithm based on classified correction is proposed for low density parity check (LDPC) codes. Different from the single correction in the normalized belief propagation (BP)-based and offset BP-based algorithms [4], the proposed algorithm utilizes two corrections for both minimum and sub-minimum magnitudes of input messages in check nodes. These two correction factors can be obtained by analyzing the offset of updated messages in check nodes between the BP and the min-sum algorithms associated with check node degree. Simulation results show that the proposed algorithm can achieve performance very close to that of the BP algorithm. Furthermore, the FPGA implementation of this algorithm can reach a throughput of 200 Mbps at BER=10-6with lower complexity and fewer resources than the BP algorithm.
Abstract This paper proposes a resource-efficient Adaptive Offset Min-Sum (A-OMS) LDPC decoder for 5G-NR Base Graph 2 (BG2) codes. A Genetic Algorithm (GA) is employed to optimize the offset factor within the check-node update, addressing the difficulty of selecting a globally optimal offset for the irregular BG2 structure. The GA operates as a meta-heuristic optimizer that minimizes the bit-error rate (BER) over a wide range of signal-to-noise ratios (SNRs). Simulation results show that the proposed GA-optimized A-OMS decoder achieves performance within 0.2–0.4 dB of belief-propagation decoding, while providing coding gains of 0.3–0.6 dB and up to a 4.6× BER reduction compared with conventional fixed-offset OMS. These improvements are obtained with approximately 35% of the computational complexity of BP decoding. The proposed approach offers a practical balance between performance and implementation efficiency for next-generation 5G-NR receivers.
This paper proposes an adaptive normalized min-sum algorithm for the decoding of low-density parity check (LDPC) codes, which utilizes an adaptive normalization factor to improve the accuracy of the soft information transferred during the iterative decoding process, and provides superior performance accordingly. This adaptive normalization factor can be adjusted dynamically and adaptively at each decoding iteration according to a look-up table obtained via training and simulation. Its implementation facility and independence from the channel characteristics make the proposed adaptive normalized min-sum algorithm expect a wide application. Simulation results show that the proposed algorithm can achieve performance much closer to the sum-product algorithm and a coding gain of around 0.2dB compared to the conventional normalized min-sum algorithm for DVB-S2's rate 2/5 and 3/5 LDPC codes over the additive white Gaussian noise (AWGN) channel.